ITM Web Conf.
Volume 59, 2024II International Workshop “Hybrid Methods of Modeling and Optimization in Complex Systems” (HMMOCS-II 2023)
|Number of page(s)
|Adaptive Intelligence: Exploring Learning in Evolutionary Algorithms and Neural Networks
|25 January 2024
Everted U-Net for 3D scene reconstruction and segmentation
Siberian Federal University,
79 Svobodny pr.,
2 Reshetnev Siberian State University of Science and Technology, 31, Krasnoyarsky Rabochy Ave, Krasnoyarsk, 660037, Russia
* Corresponding author: firstname.lastname@example.org
Abstract. The field of data science related to the processing of three-dimensional objects is becoming more and more relevant. After the successes in image processing, the apotheosis of which was the development of generative neural net-works, the intensification of efforts in the direction of three-dimensional data processing looks logical. Although there are now numerous systems for the reconstruction of three-dimensional objects and other processing, almost all of the existing solutions are aimed at working with a single object. The advances in image processing with neural networks have largely been made possible by huge datasets. There are also large datasets available for model training in this area. Freely avail-able datasets such as ShapeNet and ModelNet contain many thousands of different models, allowing for a high diversity of data. However, most of them provide single individual objects, which allows them to be used in tasks involving the processing of a single three-dimensional object, but when working with scenes containing many objects, there is often a problem of finding an appropriate dataset. This work is aimed at solving the problem of reconstruction and segmentation of three-dimensional scenes, as well as generation of datasets for the task of processing scenes from the real world.
© The Authors, published by EDP Sciences, 2024
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